Shot map

Alright, after my last post discussing team performance for the 2017 season it’s now time to dig deeper into Stratagem‘s great dataset and have a look at individual players.

As usual, it’s important to remember that Stratagem collects chances, not shots and also this: only one chance per attacking play is recorded. So for example if a team forces a goalkeeper to a series of saves in a single attack, only the highest rated chance (or a goal if it was scored) is recorded – this of course makes much sense as you can only score a maximum of one goal per attack.

Another difference from most data collectors is that whenever a blocked or saved shot rebounds and leads to a new chance, Stratagem credits the original shooter with an assist for his part in ‘creating’ this new chance. It’s important to note though that this only happens if the rebound chance happens to be of a higher quality than the original chance or end up as a goal, due to the above rule of only one chance per attack.

Lastly, when it comes to minutes played I’ve taken some time to try to calculate it as correctly as possible to get a better look at players ‘true’ performance. Sites like Soccerway seems to set their maximum playing time per match to 90 minutes which is of course wrong as there’s usually a lot of injury time to consider, sometimes even in the first half. So for this post with injury time in both halves taken into consideration you’ll see players which have played more than 30 units of 90 minutes and this also means that most players will see their per90 stats slightly diminished.

All data is open play chances, i.e. penalties are excluded for this post.

But enough of that, let’s get to it and have a look at some numbers. As usual I’ll just throw some plots at you together with my spontane thoughts:

Goalscoring

Though sharing honors as the league top scorer at 14 with Magnus Eriksson, the moral winner is Norrköping’s Kalle Holmberg with 13 open play goals while 5 of Eriksson’s goals came from penalties. Eriksson’s 9 open play goals is still very impressive though, seeing him finish joint second together with a group of strong goalscorers, all forwards – while Eriksson has mainly been used in midfield in Özcan Melkemichel’s Djurgården.

Another impressive performance comes from AIK’s Nicolas Stefanelli who managed to reach 9 open play goals despite only arriving during the summer, resulting in him topping the league when it comes to goals scored per 90 minutes. Versatile Bjørn Paulsen‘s 8 goals are equally impressive as he’s been used in both central midfield and defence alongside his starts up front for Hammarby.

Tobias Hysén shows that he’s still to be reckoned with, producing the highest total xG in the league at age 35. I’ve been waiting for his performance to drop for some years now, will he surprise me again next season?

The lack of any real xG per 90 Wizard this season (besides Stefanelli, maybe) sees some surprising names break into the immediate top. Johan Bertilsson, Skhodran Maholli (though he enjoyed an initial strong start to his arrival at Sirius) and Linus Hallenius comes to mind. Impressive of course, but it should be noted that this Allsvenskan season has been lacking the strong goalscoring box-player poacher type like pasts seasons’ Kjartansson, Owoeri and Kujovic. Kalle Holmberg could’ve been that player but IFK Norrköping’s weak end to the season has certainly limited his output to more normal levels.

Eflsborg’s Issam Jebali was the end point of most chances for the season, but when playing time is taken into consideration, AIK’s Nicolas Stefanelli once again reigns supreme.

Comparing goals and xG we see that Stefanelli’s output isn’t that much better than expected, he could very well be the real deal. Another interesting point is that Malmö’s captain Markus Rosenberg continues to underperform against xG.

Looking at how many chances players create and the average quality of those chances should give us at least some sense of their preferred attacking styles. We see here how most strong attacking players tend to cluster around an area of compromise between quality and quantity. In this group, Viktor Prodell, Johan Bertilsson, Henok Goitom and Mohamed Buya Turay tend to rely more on high quality chances (all above 0.20 xG per chance), while David Moberg-Karlsson and Stefanelli prefer to just rack up chance after chance, the latter with some respectable xG per chance as well.

Moses Ogbu is an extreme outlier with over 0.30 xG per chance, explained in part by the fact that he only took part in Sirius’ very strong first half of the season before getting injured. Still a very interesting player, his numbers would likely have dropped a bit had he been fit to play when Sirius struggled (including 7 straight losses) in the second half of the season.

Chance Creation

Elfsborg’s Simon Lundevall provided most assist overall but taking playing time into account, IFK Norrköping’s Niclas Eliasson was Allsvenskan’s main creator this season. Racking up 11 assists in the first half of the season before leaving for Bristol City in the Championship, his departure effectively ended Norrköping’s top 3 ambitions.

Magnus Eriksson, Tobias Hysén and Nahir Besara‘s appearance in the Assists Top 10 really shows their versatility and huge importance to their teams’ overall attack.

Just like seen with goals above, some interesting and perhaps surprising names appear when we account for playing time. I certainly didn’t expected to see Sirius’ Elias Andersson or AFC Eskilstuna’s Andrew Fox here, but there you go.

Ken Sema‘s strong finish to the season saw him (besides earning a call-up to ‘Party-‘ Janne Andersson’s national team which beat Italy to advance to the World Cup) top the Expected Assists table at roughly 11, though 3 less than his actual output. Sema has also been performing well in Östersund’s Europa League campagin and is one of many players they’ll have to work hard to keep over the winter transfer window.

Nostalgic as I am, it’s certainly nice to see my boyhood hero Kim Källström racking up some strong numbers placing him in the Top 10 Assists and xA tables, as well as creating most chances in the league overall and 4th most when taking playing time into consideration.

Comparing assists and xA we see how Niclas Eliasson has been outperforming his expected output (likely thanks to some effective scoring from Kalle Homberg) while Ken Sema has been underperforming. Lundevall is closer to his expected output.

Just like with the Attacking Styles, Player Chance Creation Styles are mostly clustered with a lot of creative players combining reasonable quality with quantity. Ken Sema, Elias Andersson and Yoshimar Yotún (who left Malmö for the MLS in the summer) are the extremes when it comes to creation volume, while Andreas Vindheim has created some very good chances for Malmö.

Attacking Production

By combining goals and assists into Attacking Production we see that Besara was the most productive player when it comes to raw numbers, but when factoring in playing time, Stefanelli once again tops the table in both expected and actual output. Prodell has done well considering his playing time, as well as Malmö’s Alexander Jeremejeff who’s second behind Stefanelli in xG+A per 90 minutes.

Djurgården’s both wingers break into the Total Chance Production table, with Othman El Kabir joining Eriksson just below the top trio. Paulinho was the most productive attacking player though, creating over 5 chances per 90 minutes for Häcken.

Looking at actual and expected output, we see how most strong attacking players like Besara, Jebali, Homberg, Eriksson, Hysén and Stefanelli tend to perform close to what we can expected. Eliasson is again overperforming while Rosenberg is doing the opposite. Eric Larsson is worth a special mention here as he has produced some fine numbers for a fullback, with his underperformance coming largely from his teammates in Sundsvall underperforming on the chances he created for them.

Seperating Expected Goals and Expected Assists let us see how the attacking players specialise. Once again we see how this season has really lacked many strong specialist, with only Stefanelli and Sema really standing out on their ends. Most players tend to cluster somewhat here as well, combining creativity with being at the end of chances as well.

Player Profiles

As I now work with StrataData, I’d thought I’d do a total revamp of the popular player maps. The style is more or less shamelessly stolen from a range of other analysts, no names mentioned, and now also include Chance Creation Maps:

As mentioned earlier, Kalle Holmberg was this season’s strongest goalscorer, and from his Chance Map it’s easy to see why: he usually gets into some very good positions just in front of goal, with an average xG of 0.19 per chance. 13 open play goals is strong, but as I’ve also mentioned I think he could’ve done even better had IFK Norrköping’s performance not dropped (and Niclas Eliasson not left).

Operating from Djurgården’s right wing, Magnus Eriksson was another strong goalscorer this season, though a bit more versatile as he also provided a lot of assists for his team. Mostly crosses from the right flank but also two shot rebounds. His Chance Map is a bit different from Holmberg’s with more chances outside the box, which is only natural as he’s after all a midfielder. Though attacking is certainly his main quality, Djurgården will also miss his work ethic, grit and competitiveness now that he’s left for the MLS.

Veteran Tobias Hysén continues to be extremely important to IFK Göteborg’s attack. His Chance Map combines a lot of good chances inside the box with some poorer outside, some of them direct free kick. When it comes to Chance Creation he’s provided some crucial passing inside and into the box, as well as some corners and free kicks.

Örebro’s Nahir Besara was also extremely important to his team’s attack, combining some chances inside the box with a lot of shooting from outside, including one goal from a direct free kick. His creation numbers are boosted by three rebounds who turned into goals, otherwise it’s mostly corners and crosses into the box.

Nicolás Stefanelli arrived at AIK at a crucial time this summer, with the team’s attack struggling during the first half of the season. The Argentinian took some time to adopt but slowly turned into to a real strong presence up front, scoring 9 goals from 14 starts. It will be very interesting indeed to see if he can continue his fine performance come the new season. As a Djurgården supporter, I sure hope not.

Linus Hallenius is an interesting case that’s flown under at least my radar this season. With 7 goals and nearly 10 xG he’s done well for a struggling Sundsvall side that just barely managed to stay up. Most of his chances have been created by Eric Larsson, so it’ll be very interesting to see if Hallenius can continue his fine performance next season with the right back having left for champions Malmö.

Elfsborg’s Simon Lundevall was the assist king this season at 12, with 4 of them coming from corners, curiously with some rather high xA values – 3 of them are above 0.30 xA. Maybe Elfsborg have some corner strong routine going on? Lundevall has also provided some long range passes on the left half of the pitch, which I guess is related to counter-attacking.

Niclas Eliasson’s strong first half of a season earned him a move abroad, and as mentioned earlier IFK Norrköping never really looked the same after that. Overperfoming, sure, but he did create some really good chances for his team with his precise crossing from both flanks.

Ken Sema was another creation monster, racking up some really good chances with an average xA per chance of 0.18. It’s clear to see why, as most of his passes was either directly inside the box, or ending up in it – a direct consequence of Östersund’s heavily passing-oriented style of attack.

Though it stopped at just one season before he chose to end his career, Kim Källström’s long-awaited return to Djurgården was (despite some very inconsistent perfomances) instrumental in returning the team he once won the league with in two consecutive seasons at the start of the millenium, back to the top 3. When he was at his best this season, sitting back in his deep-lying playmaker role he dictated much of Djurgården’s attack with his quarter-back ‘Hail Mary’ style of long passing. Interestingly though, all his assists came from set pieces where he got more time to use his precise left foot.

I mentioned Eric Larsson before and looking at his Chance Creation Map we see clearly how strong a player he is. From his right back position at struggling Sundsvall he produced 52 chances and well over 6 xA – more than most midfielders. Though his teammates only managed to score twice on these chances, with his move to Malmö I expect him to get a lot more assists next season.

That’s it, thank you for reading the whole piece. If you want to see any more Player Chance/Creation Maps, just let me know on Twitter.

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A week ago I published the first part of – hopefully – three Allsvenskan 2016 summaries, then focusing on team performance. Now it’s time to have a look at individual players, much like I did back in July. Though there now exists detailed Opta data for Allsvenskan, my work on this site has mostly been based on the older, less detailed data sources focused on shots and thus this summary will only look at attacking players.

I’ve again had a look at Goal Contribution (goals+assists) and Expected Goals, dividing all players into three age groups, and also had a closer look at a few interesting players:

The Goal Contribution chart is unsurprisingly headed by Häcken’s John Owoeri who clinched the title as the league’s top scorer with his 4 goals against Falkenberg in the last round of the season. Interestingly, Owoeri only came alive in the second half of the season, scoring 15 of his 17 goals after the summer break.

Assist monster Magnus Wolff Eikrem sits in second, with his 0.71 assists per 90 minutes playing a big part in Malmö retaking the title. Of the other top players, Antonsson, Kjartansson and Nyman left the league during the summer transfer window but still impressed enough during the spring to remain in the top 10.

Djurgården’s Michael Olunga sits top among the players aged 20-23. Dubbed ‘The Engineer’ for his ongoing studies, Olunga just like Owoeri needed time to get going, scoring all of his 12 goals during the last 13 games when Mark Dempsey came in to steer Djurgården away from the relegation battle.

Comparing Owoeri and Olunga, it’s clear from the shot maps why Owoeri was the superior goalscorer this season. He only shoots slightly more than Olunga, but does so from far better locations closer to goal, with his average xG per shot at 0.16 while Olunga at 0.12 rely more on his finishing skill from longer range. If ‘The Engineer’ can work on his shot selection for next season I really think he can challenge for the top scorer title.

AIK’s Alexander Isak reign supreme among the youngest players, with his 0.62 G+A90 very impressive for a player who only turned 17 late in the season. He’s quite good at getting into good shot locations as well, with 5 of his 10 goals coming from a sweet spot just in front of goal.

There’s been plenty of rumours of an upcoming big transfer during the winter window and looking very much like the real deal, Isak could very well break Zlatan Ibrahimovic’s transfer record from 2001. Here’s a nice radar plot from Ted Knutson showing Isak’s skills:

Malmö’s Vidar Kjartansson was the king of xG this season, and the club impressingly still managed to secure the title after selling him during the summer transfer window. Kjartansson combined both quantity with quality, taking most of his shots from very good locations with an average xG per shot of 0.2.

Östersund’s Abdullahi Gero was a bit of a surprise for me, but his shot locations are good with an average xG per shot close to Kjartansson at 0.19. He could very well go on to score more next season if given the chance in Graham Potter’s Östersund side which have done so well xG-wise this season – actually finishing 4th in xG Difference per game!

As a Djurgården supporter I’m glad to see 20-year old Tino Kadewere’s development this season. Though his 793 minutes played was less than the 900 needed to be included above, he racked up an impressing 0.79 G+A90 which would see him sit 8th overall, just above Olunga, and top the players aged 20-23 if the cut-off would have been 1/4 of the league minutes played instead of 1/3. Focusing more on assists than Olunga, the two could form a dynamic partnership for Djurgården if they get the chance next season.

That’s it for now, but if you want to see more shot maps, just give me a shout on twitter. If I’ll find the time, I’ll also write a third summary looking at how my predictions have done over the season and how my model did against the betting markets.

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With Allsvenskan back in action after the summer break and the transfer window approaching, I thought it would be interesting to take a look at some individual player stats. Some players have already left the league for new challenges abroad, and when the window opens on July 15th we’ll likely see some moves between Swedish clubs as well. Given the limitations of my database, this post will focus on attacking players as the most advanced data available is shot coordinates.

I usually don’t write about Sweden’s second tier Superettan, simply because I don’t follow it at all, but as there are several players who could move to Allsvenskan and even abroad, I’ve also looked at players from this league.

So let’s start with taking a look at which players have produced the highest number of goals, assists and Expected Goals so far this season. I’ve only included players who have played 1/3 of the league so far (390 and 450 mins for Allsvenskan and Superettan, respectively) and also looked closer at younger players, splitting them into two groups: players aged 20-23 and players aged up to 19.

Allsvenskan

Player profiles

There are some players in the plots above worth looking closer at. Let’s start out with last year’s top scorer, Emir Kujovic:Kujovic has just recently signed with Belgian side Gent, leaving reigning champions Norrköping after a couple of highly productive seasons. His goal and xG output this season are on par with last season’s but he’s also doubled his assists per 90 minutes, having been involved in almost a goal per game this season. I don’t know much about Belgian football, but given the right kind of attacking style he may very well score some goals over there.

Häcken’s Paulo De Oliveira, or Paulinho as he’s called, leads the league in assists+goals per 90 with his impressing 1.13 made up of just goals. Having overperformed against xG since his return to Swedish football, Paulinho may very well be the best finisher in the league.

With Malmö struggling to capitalise on their xG last season, Vidar Kjartansson has grown into a very good signing for them. I haven’t seen him played that much, but given his shot profile he looks just like the strong center forward scoring from mostly inside the box they needed to gradually phase out the aging Markus Rosenberg.

Struggling to take a regular spot at Malmö for the last couple of seasons, Pawel Cibicki’s move to newcomers Jönköpings Södra has worked out well for him. With more playing time, he has continued to improve his goal output and given his age he could be on his way to a bigger club abroad soon.

One who has already taken the next step is AIK’s loan Carlos Strandberg, who after struggling at Russian CSKA Moscow returned to Sweden this season. The young and forceful striker continued where he picked up and has been crucial for his club this season. From his shot profile we can see that he favours shooting from the left, mostly due to his powerful left foot. Set to return to Russia soon, Strandberg will make his last game against Malmö this weekend.

Leading the youngest players in xG per 90 is another AIK striker, 16 year old Alexander Isak, who has impressed so far and is supposedly targetted by a number of big European clubs. Yeah, the sample size is small but given his very young age he’s done well and should he continue to improve he could grown into a class player.

Superettan

As I’ve said, I very rarely watch Superettan so I know next to nothing about most players in the league. Here I’ve just picked out a few interesting players from the ranking below to look closer at, and I’ll leave the shot profiles uncommented.

That’s it for now, but if you want to see shot profiles from any other player in these leagues, just hit me up on Twitter.

With the regular Swedish season being over and Norrköping crowned champions, all that’s left now is to decide who’ll get the last spot in next years Allsvenskan. In this qualification play-off, Sirius finishing 3rd in Superettan is pitted against Allsvenskan’s 14th placed Falkenberg in a two-game battle.

Let’s have a look at some stats for the teams, compared to both the teams in Allsvenskan (blue) and Superettan (red):

From this graph, Sirius actually look really good with especially a strong defensive, even when compared to the Allsvenskan teams, while Falkenberg’s defence looks really poor. However, this doesn’t say much about how the teams compare to each other since Falkenberg has had to face far tougher opponents in Allsvenskan.

Looking at the xG maps what again stands out is the defensive performances of the teams. While Falkenberg have conceded a massive 415 shots, almost 14 per game, Sirius have only conceded 241 shots or about 8 per game. Not only that, Sirius’ xG per conceded attempt is 0.111 while Falkenberg’s is a staggering 0.154, meaning they concede shots in quite bad (for them) situations – not a good thing.Looking at individual players we can se how Sirius’ Stefan Silva is the big overperformer here with his 12 goals almost doubling his xG numbers. Also, Falkenberg seem to have more goalscoring options with three players over 6 goals while Sirius only have Silva.

As always, I’m not willing to present any prediction for individual games, but here I had hoped to show the results of a simulation covering both play-off games including possible extra time and penalty shoot-out. I have run such an simulation, however I’m not happy with the results as my model seems to be favouring Sirius too heavily. This is almost certainly due to the different leagues involved, making Sirius look way better than they would be against an Allsvenskan side. Since I only came up with writing this post this morning, I haven’t had the time to look into a possible league strength variable to use in the simulation.

But if I had to guess, I’d say that Sirius looks like a real strong side and should possibly be considered favourites for promototion here, mostly due to Falkenberg’s nasty habit of conceding a lot of shots with high goal expectancies.

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Monday night AIK will host IFK Göteborg for an extremely important game in the race for the Allsvenskan title. Both teams are close behind Norrköping in the lead and will surely go for the win here to challenge for the title, and I thought it would be a good idea to have a look at some team stats as a preview to this crucial game.

The plot below contains goals, shots, Expected Goals, xG per attempt, goal conversion % and shot on target % – both for and against, normalized per game where necessary. Home and away stats for each team in the league are separated with home in blue and away in red. For each subplot the lower right corner is preferable, with high offensive and low defensive numbers.

Besides SoT%, both AIK and Göteborg appear to be among the best in the league in each stat, which partly explain why they are fighting for the title. What is really striking though, and could be seen as a indicator of team style, is that while AIK’s offensive numbers at home are really good, Göteborg’s strength when playing away is their defence.

This is also evident from each teams xG maps, where it is clear that AIK’s main strength is their attacking power and ability to produce high volumes of shots with high xG values each game. Göteborg on the other hand rely heavily on their defensive skills to protect their box and limit the opposition’s scoring chances. This clash of styles adds yet another interesting flavor to an already interesting game.

Looking at each teams top 5 goalscorers it is clear that AIK’s impressive attack rely heavily on Henok Goitom. His 16 goals this season are pretty much in line with his xG of about 15 while Göteborgs Søren Rieks seems to be overperforming with his 10 goals equalling almost two times his xG numbers. Both teams have sold one of their best offensive players with Bahoui and Vibe both making a move abroad this summer.

What about a prediction then? While I won’t reveal any percentages for this (or any) game, what I can say is that my model is pretty much in tune with the betting market. AIK is a slight favourite due to their home advantage, but this is really anybody’s game and it will hopefully be highly entertaining.

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In my last post I discussed the concept of Expected Goals and how its probabilistic nature opens up for simulations. Today I’m going to talk about another cornerstone when building my model – the data. I do this because I think it’s important to fully explore the data when building a model, to understand its strengths and weaknesses, its advantages and limitations and how these affect the model and its output and performance. No model is perfect, but if we’re aware of its biases and limitations we can still make good use of it.

While Opta produces very advanced data covering every on ball event in the bigger leagues, the data available for Swedish football is lesser in terms of detail, quality and reliability. What’s available for use is pretty much just shots, and there is no distinction between different types of shots besides penalties. Only shots that ended up as goals have detailed information on whether it was headed, came from a set piece and so on. Using this information would result in a skewed model, rating for example headers too high since every existing header is also a goal. I’ve therefore treated all these types of situations as regular shots. Furthermore the location of the shots is recorded with less accuracy than Opta’s. The x and y coordinates are recorded with only integers, making them less precise and the location of the shots is sometimes plain wrong. I regularly examine the shot maps of games I’ve watched live and there always seems to be some errors, but I’m hoping these will be insignificant. There’s no information on passes, defensive actions or anything like that, the only events recorded besides shots is fouls, corners, offsides, substitutions and cards.

Data exists for the top league Allsvenskan, but also second tier Superettan and the two Division 1 leagues below it, from season 2011 and onwards. However, the data from Division 1 seems to be of too poor quality for modelling and substitutions were not recorded properly until season 2013, so per90 stats from seasons 2011 and 2012 are pretty much useless. Anyway, here’s a shot map of every shot recorded for Allsvenskan and Superettan from season 2011 up till now.

With so many shots taken from the exact same locations, it’s probably easier to get a sense of the distribution of the shots through a hexbin plot, showing what could be described as the shot density of every location on the pitch:

As we can see, the penalty box and the area just in front of it seems to be the most frequent shooting locations, which makes sense. Also, the penalty spot stands out with so many shots taken from the exact same location.

Looking at only goals, the penalty spot again stands out but we can also see that most goals are scored inside the box, especially from more central locations. This again makes sense.

It’s also a good idea to take a look at the general characteristics of the games you want to model, so I’ve created some histograms of goal and shot distributions from Allsvenskan.

Examening these, we can see that an average game ends up with a total of 2.74 goals, with the home side having a 0.433 goal advantage. What about shots?

As expected, the home side also enjoy an advantage when it comes to shots, about 2.481 on average, while the average total number of shots in an Allsvenskan game is 21.931.

I think we have a good sense of the league and games we want to model now, so I’ll end this post here. Next up I’ll get down to business, building the model and putting it to the test.

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Though I had planned not to share any shot maps before I had discussed the concept of Expected Goals and my model properly first, after watching last night’s game between Norrköping and Djurgården I just couldn’t help myself. Now some of you probably have seen this kind of plot before so I will leave out the explanation for later posts.

Here’s last night’s high-scoring game. Being a lifelong Djurgården supporter this was not a pleasant game to watch, and to add to the pain I actually had a bet on the under here. Sigh.

While I don’t believe much in year to year trends such as Team A vs. Team B always produces a lot of goals in today’s modern football where players and managers change teams frequently, watching the game I got a vague feeling of déjà vu and just had to look up this fixture from the last few years.

Looking at the games from seasons 2014 and 2013, it seemed there is some truth to the myth. But while it may look like this particular fixture usually end up a high scoring affair, in the other seasons in my database (2011 and 2012), the games ended 2-1 and 1-1 respectively. Furthermore, Djurgården actually only had two players starting in all three games: Kenneth Høie and Emil Bergström. The same goes for Norrköping with only David Mitov Nilsson and Andreas Johansson starting all three games.

With so few players playing all three games and the games therefore being played under completely different preconditions, I think we can safely put this high-scoring trend down to pure coincidence. I still feel like a fool for betting the under though.